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1 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
HORIZON SCAN 2APRIL, 2017
1
© 2017 Rural Industries Research and Development Corporation. All rights reserved.
ISBN 978-1-74254-956-9 ISSN 1440-6845
Horizon Scan 2 Publication No. 17/033 Project No. PRJ-010512
The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances.
While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication.
The Commonwealth of Australia, the Rural Industries Research and Development Corporation (RIRDC), the authors or contributors expressly disclaim, to the maximum extent permitted by law, all responsibility and liability to any person, arising directly or indirectly from any act or omission, or for any consequences of any such act or omission, made in reliance on the contents of this publication, whether or not caused by any negligence on the part of the Commonwealth of Australia, RIRDC, the authors or contributors.
The Commonwealth of Australia does not necessarily endorse the views in this publication.
This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to RIRDC Communications 02 6923 6900.
Researcher Contact Details
Name: Dr Grant Hamilton Address: QUT, GPO Box 2434, Brisbane, QLD 4001
Phone: +61 3138 2318 Email: [email protected]
In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form.
RIRDC Contact Details
Rural Industries Research and Development Corporation Building 007 Charles Sturt University Boorooma Street Wagga Wagga NSW 2650
C/- Charles Sturt University Locked Bag 588 Wagga Wagga NSW 2678
Phone: 02 6923 6900 Fax: 02 6923 6999 Email: [email protected]. Web: http://www.rirdc.gov.au
Electronically published by RIRDC in August 2017 Print-on-demand by Union Offset Printing, Canberra at www.rirdc.gov.au or phone 1300 634 313
Horizon Scan 2: April, 2017. Report by Queensland University of Technology for Rural
Industries Research and Development Corporation, Australia.
REPORT CONTRIBUTORSDr Grant Hamilton, Dr Levi Swann, Dr Sangeetha Kutty, Prof Greg Hearn, Assoc Prof
Richi Nayak, Dr Markus Rittenbruch, Prof Roger Hellens, Dr Debra Polson, and Dr Jared
Donovan.
ACKNOWLEDGEMENTSThe research provider Queensland University of Technology acknowledges the financial
assistance of the Rural Industries Research and Development Corporation in order to
undertake this project.
The contributions of members of the Australian rural industries, who gave their time to
participate in surveys as part of this research, are also acknowledged.
3 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
TABLE OF CONTENTS
Executive Summary
Introduction
Approach
Results
Digital Twin
Low Power Wide Area Networks
Human-Machine Interfaces
Solar Retransmission
Personal Analytics
Conclusion
References
Appendix: Approach and Project Streams
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4 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
EXECUTIVE SUMMARY
Technology advancement and technology driven disruption is expected to experience
exponential growth in the coming decades. Although Australian agriculture has a strong
track record of technology integration, the speed of technology innovation makes the
scanning and identification of high impact emerging technology a priority for Australian
rural industries. This report responds to this priority, and presents the outcomes of a
methodology to scan, synthesise and communicate technology trends and innovations
that may impact Australian rural industries.
In this first iteration, a broad range of category and discrete technology types have
been identified. Led by data mining and synthesis, and assisted by QUT experts and
innovative individuals identified by RIRDC, a sub-set of these technologies has been
refined. Presented in this report are the most highly rated technologies, positioned at the
confluence and fringes of technology megatrends including the Internet of Things, big
data, artificial intelligence and advanced sensors.
DIGITAL TWINA digital twin is a dynamic software model that represents a physical asset or process.
Generated by sensors installed in physical assets and processes, a digital twin allows a
user to view and act on real-time data remotely. Simulations can be run using a digital
twin to predict asset or process failure. Increasing implementation of smart devices
means that digital twins can potentially represent highly complex processes and be
transformative in nearly all industries.
5 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
LOW POWER WIDE AREA NETWORKSLow power wide area networks (LPWAN) support communication between sensors
and smart devices. We have highlighted a type of LPWAN called LoRaWAN.
LoRaWAN technology uses unlicensed radio frequencies and therefore can support
the interoperability of smart devices and sensors without reliance on cellular networks.
This positions LoRaWAN as a significant technology for supporting Internet of Things
systems in remote locations.
HUMAN-MACHINE INTERFACESHuman-machine interfaces enable communication between humans and machines. We
have highlighted three types of human machine interfaces. Natural language interfaces
utilise sensory and non-sensory inputs such as touch, sight, and radar to facilitate
communication between humans and machines. Conversational interfaces enable
communication between humans and machines through spoken or written language.
Wearable user interfaces allow communication between humans and machines through
input collected from the user or environment such as fatigue and location. Innovative
human-machine interfaces have the potential to remove complexity from interactions
with machines, allowing unskilled workers with poor computer literacy to perform
complex tasks.
SOLAR RETRANSMISSIONSolar retransmission is a patented technology for using satellite technology to capture
and retransmit sunlight for use on Earth. The sunlight is captured in space and then
retransmitted in frequency bands optimal for photosynthesis. This technology is viewed
as an expansion on the capabilities of precision agriculture and sits alongside proposals
for lunar-based solar energy and mineral collection.
PERSONAL ANALYTICSPersonal analytics technologies monitor personal activity and behaviour. Two
implementations of personal analytics technologies are presented. Labour tracking
leverages personal analytics technologies, such as wearable sensor devices, to track
worker fatigue, stress, and movement. Smart contact lenses are an emerging personal
analytics technology. Worn just like normal contact lenses, they are capable of monitoring
biometric signals, as well as enhancing vision with augmented reality. Smart contact
lenses represent the emergence of nanosensors for personal analytics and labour
tracking. The innovation of unobtrusive wearable devices that use nanosensors is
potentially transformative in a range of domains.
Successive iterations of the methodology will be employed in future reporting periods to
supplement the already identified technologies. Findings will continue to be extrapolated
to clearly communicate the transferability and subsequent impact of key emerging
technologies for Australian rural industries.
6 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
INTRODUCTION
In this project, we are developing a scanning capability to assist the early detection,
analysis and forecasting of issues and opportunities that may impact Australian rural
industries. This will support rural industries and the range of supporting leaders and
organisations to make sound policy and investment decisions that will position Australian
rural industries well into the future.
Horizon Scan 1 worked toward the project aims by developing and then implementing
a horizon scanning method comprising of interdisciplinary expertise, data mining and
visualisation. This method was used to extract and synthesise potential issues and
opportunities in the context of five broad focus areas representing the imperatives of
Australian rural industries. The outcomes of Horizon Scan 1 were directed toward one of
these focus areas, market and consumer preferences, and resulted in the identification
of seven key trends.
Horizon Scan 2 incorporates learnings from Horizon Scan 1, and builds on the capabilities
developed. Emphasis has been placed on revising the approach to accentuate trend
discovery, evaluation and consolidation. Perhaps most significantly, Horizon Scan 2
adopts a refined scope, drawing on the previously identified megatrend of transformative
technologies. The Rural Industries Futures Megatrends report outlined that transformative
technologies will have significant impact on almost all facets of Australian rural and
agriculture industries (Hajkowicz & Eady, 2015). While Australian agriculture has a
track record for successful and timely adoption of technology, technology advancement
and disruption is likely to experience exponential growth over the coming decades. The
7 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
speed at which this disruption occurs makes the timely identification of technologies and
the analysis of their potential impact on Australian rural industries particularly important.
In each of the reports produced during this project, we look to supplement the
knowledge base already established in the Rural Industries Futures Megatrends report,
and subsequent technology factsheets developed by RIRDC. Specifically, we will target
emerging technologies that are impacting a range of industries in an international
context. This report represents the first iteration of this direction. Given the shift in
scope and revision of methodology, the following section presents a brief outline of the
approach used for Horizon Scan 2 before presenting the technologies identified during
this reporting period.
8 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
APPROACH
This project employs a concurrent phase, iterative methodology to:
1. scan and extract data from a variety of sources;
2. synthesise this information to identify potential trends and innovations for further
investigation;
3. and, communicate these issues through visualisations.
Implementation of each respective project phase, led by data mining, synthesis, and
visualisation, occurs across three interactive streams: Discovery; Evaluation; and,
Consolidation. These streams are continuous over the course of the project requiring
different inputs from each project phase. The inputs from each phase, and their
interactivity is represented in Figure 1. A more detailed description of this approach is
included in the Appendix: Approach and Project Streams.
DISCOVERYImplementing data mining and human synthesis, the discovery stream focused on
scanning selected data sources, including twitter, patent databases, industry and market
reports, and technology news media. The confluence of data mining and synthesis
outputs in the discovery stream resulted in a list of emerging technologies for further
investigation in the subsequent evaluation and elaboration streams.
9 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
EVALUATIONLeveraging QUT experts and innovative farmers identified by RIRDC, the evaluation
stream focused on filtering the signals identified in the discovery stream. This was
achieved using online surveys in which experts were asked to rate the potential impact
that a broad range of emerging technologies will have on Australian rural industries.
This process has assisted us to narrow the scope of technologies we have presented
in this report, and that will be included on the watchlist for monitoring throughout future
reporting periods.
CONSOLIDATIONThe consolidation stream focused on further investigating and monitoring technologies
on the initial watchlist. This involved a detailed analysis of the technologies’ potential
impact for Australian rural industries and developing a rationale for why they should be
monitored. Both data mining and synthesis are essential for this stream to receive input
from a broad range of data, and gain the requisite detail. Visualisation is used to draw
together and present inputs from data mining, synthesis, and evaluations provided by
experts.
FIGURE 1. PROJECT INPUTS AND STREAM INTERACTION
Sourc
es
Data
minin
g
List o
f tech
nolog
ies
Surve
y
Sourc
es
Surve
y
Synth
esis
Synth
esis
Data
minin
g
Synth
esis
List o
f tech
nolog
ies
Surve
y res
ults
DISCOVERYEVALUATIONCONSOLIDATION
Visua
lisati
on
Synth
esis
Data
minin
g
10 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
iot
sensorsnetworks
big_data
artificial_intelligenceadvanced_materials
automation
cloud_computing
data_analytics
renewable_energy
ambient_intelligence
digital_health
genomics
machine_learning
cybersecurity
nanotechnology
wearables
ai
automated_finance
quantified_self
computer_vision
human2machine
imagery
industry_4.0
telecommunication
3d_printing
immersive_technology
neural_networks
personalised_medicine
predictive_analytics
synthetic_foods
telematics
autonomous_vehicles
customer_journey_analytics
deep_learning
digital_business
digital_finance
digital_mesh
future_of_food
infomatics
information_modelling
labour_tracking
mobile_dominance
natural_language_interface
natural_language_interfaces
neuromorphic_hardware
pattern_recognition
personal_analytics
sattelites
transportation
4d_printing
adaptive_security_architecture
affective_computing
ambient_user_interfaces
app_economy
assisted_selection
augmented_reality
aware_computing
bg_data
biomarkers
biometrics
biosensing
bitcoin
blockchain
botnets
brain
broadband_capable_sattelites
caavo
cell_cultured_milk
coating_for_frost_prevention
cognitive_computing
collaborative_robots
computer_interface
context
context_brokering
conversational
cryptocurrency
cyber_defence
cyber_insurance
dark_data
data_brokering
data_governance
data_storytelling
data_virtualisation
device_mesh
dig_data
digital_payments
digital_twin
distributed_ledger
driverless_vehicles
drone_delivery
energy_farming
future_of_work
genetic_modification
genome_medicine
genome_sequencing
graphene
green_crude
heuristic_automation
human_augmentation_technology
hyperloop
ibm_watson
industrial_iot
infonomics
infosecurity
insect_protein
intelligent_apps
internet_of_health
ion_batterieslong_range_wide_area_network
machine2machine
machine2machine_communication
machine_intelligence
marker
mesh_app_and_service_architechture
microbiome
mixed_reality
mobile_technology
modelling
modile_dominance
nanotechnology_energy_generator
nci_cloud
neromorphic_computing
network_ubiquity
networks_
neuromorphic_computing
novel_materials_for_3d_printing
organic_solar_cells
personl_analytics
platforms
precision_medicine
predictive
printed_nanotechnology
robotics
sattelite_sun_capture
shadow_it
smart_cities
smart_contact_lenses
smart_dust
smart_farm
smart_grid
smart_home
smart_workplace
sodium
software_innovation
space_exploration_and_presence
speech_analytics
speech_recognition
stem_cells
synthetic_biology
synthetic_wine
systems_medicine
tech_sequencing
tellecommunication
tomato_genome_sequencing
unmanned_aerial_vehicle
vehicular_network
vertical_farming
virtual_reality
voice_recognition
volumetric_displays
wearable_user_interface
wireless_electric_vehicle_charging
wireless_unification
RESULTS
FIGURE 2. VISUALISATION OF DISCOVERY STREAM OUTPUT
11 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
iot
sensorsnetworks
big_data
artificial_intelligenceadvanced_materials
automation
cloud_computing
data_analytics
renewable_energy
ambient_intelligence
digital_health
genomics
machine_learning
cybersecurity
nanotechnology
wearables
ai
automated_finance
quantified_self
computer_vision
human2machine
imagery
industry_4.0
telecommunication
3d_printing
immersive_technology
neural_networks
personalised_medicine
predictive_analytics
synthetic_foods
telematics
autonomous_vehicles
customer_journey_analytics
deep_learning
digital_business
digital_finance
digital_mesh
future_of_food
infomatics
information_modelling
labour_tracking
mobile_dominance
natural_language_interface
natural_language_interfaces
neuromorphic_hardware
pattern_recognition
personal_analytics
sattelites
transportation
4d_printing
adaptive_security_architecture
affective_computing
ambient_user_interfaces
app_economy
assisted_selection
augmented_reality
aware_computing
bg_data
biomarkers
biometrics
biosensing
bitcoin
blockchain
botnets
brain
broadband_capable_sattelites
caavo
cell_cultured_milk
coating_for_frost_prevention
cognitive_computing
collaborative_robots
computer_interface
context
context_brokering
conversational
cryptocurrency
cyber_defence
cyber_insurance
dark_data
data_brokering
data_governance
data_storytelling
data_virtualisation
device_mesh
dig_data
digital_payments
digital_twin
distributed_ledger
driverless_vehicles
drone_delivery
energy_farming
future_of_work
genetic_modification
genome_medicine
genome_sequencing
graphene
green_crude
heuristic_automation
human_augmentation_technology
hyperloop
ibm_watson
industrial_iot
infonomics
infosecurity
insect_protein
intelligent_apps
internet_of_health
ion_batterieslong_range_wide_area_network
machine2machine
machine2machine_communication
machine_intelligence
marker
mesh_app_and_service_architechture
microbiome
mixed_reality
mobile_technology
modelling
modile_dominance
nanotechnology_energy_generator
nci_cloud
neromorphic_computing
network_ubiquity
networks_
neuromorphic_computing
novel_materials_for_3d_printing
organic_solar_cells
personl_analytics
platforms
precision_medicine
predictive
printed_nanotechnology
robotics
sattelite_sun_capture
shadow_it
smart_cities
smart_contact_lenses
smart_dust
smart_farm
smart_grid
smart_home
smart_workplace
sodium
software_innovation
space_exploration_and_presence
speech_analytics
speech_recognition
stem_cells
synthetic_biology
synthetic_wine
systems_medicine
tech_sequencing
tellecommunication
tomato_genome_sequencing
unmanned_aerial_vehicle
vehicular_network
vertical_farming
virtual_reality
voice_recognition
volumetric_displays
wearable_user_interface
wireless_electric_vehicle_charging
wireless_unification
For Horizon Scan 2, we present results for one cycle of each the discovery, evaluation and
synthesis streams. Data mining and synthesis outputs from the discovery stream have
led to the identification of a broad range of technologies. These identified technologies
can be classified as either category technologies or discrete technologies.
Category technologies describe broad classifications of technology, while discrete
technologies describe specific implementations of technology, and they are generally
related to one or more category technology. Examples of the relationship between
category and discrete technologies is shown in Figure 2, which is a visualisation of a
segment of the discovery stream outcome. Category technologies, such as Internet of
Things, sensors and networks are represented by large nodes. These large category
technology nodes share many connections with discrete technologies, which are
represented by the smaller nodes.
The category and discrete technologies identified during the discovery phase form the
initial pool from which we have selected technologies for evaluation and consolidation. For
the Horizon Scan 2 reporting period, a range of discrete and category technologies were
selected for further investigation. The remaining pool of technologies will be investigated
in successive cycles of the methodology. Moreover, as the project continues, successive
cycles of the discovery stream will identify and introduce new technologies for further
evaluation. Select technologies that are evaluated to have high potential impact will be
monitored to track their development over the course of the project.
12 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
DIGITAL TWIN
A digital twin is a dynamic software model of a physical thing or system (Cearley, Walker,
& Burke, 2016). It is connected to and uses data from sensors that are installed in
the physical object or system that is being represented (Feuer & Weissman, 2016).
In addition to using data from sensors integrated in physical things, digital twins can
integrate contextual data, including metadata, condition or state data such as location
and temperature, event data, and analytics in the form of algorithms and rules (Cearley
et al., 2016). By utilising this data, a digital twin can represent the structure, context and
behaviour of a physical thing or system in digital space.
APPLICATIONDigital twins provide an interface that allows a user to understand past and present
operation of a thing or system (Feuer & Weissman, 2016), and then respond to
any changes as necessary (Cearley et al., 2016). As data is collected over time, the
application of a digital twin becomes more powerful and allows for greater intelligence
within a system. For example, data collected over time can be used to monitor equipment
and predict part failure, thus enabling a shift from a preventative maintenance model to
condition-based asset management (Cearley et al., 2016). Beyond this, a digital twin
can be used to virtually simulate, make predictions about, validate, and optimise an entire
production system’s future operations (Feuer & Weissman, 2016; Volkmann, 2016)
Digital twins have typically been used in high-value asset industries, such as manufacturing
and transportation (Velosa, Schulte, & Lheureux, 2016). However, the very idea of a
digital twin and its capabilities is shifting with the advancement of the Internet of Things
space. Offering the ability to digitise physical space, a digital twin provides a bridge
between the physical and the digital world (Volkmann, 2016). General Electric is driving
digital twin innovation, and has created a digital interface for managing a wind farm.
The interface represents digital equivalents of each wind turbine in the wind farm, and
provides various information about the operating environment and conditions of the
wind turbines. Controls are also present on the interface that allow configurations to be
made to optimise performance (Lund et al., 2016).
FORECASTIn the coming years, sensors and ‘intelligence’ will increasingly be present in common
assets such as buildings, cars, and consumer products, and digital twins will exist for
potentially billions of these things. Given this, digital twins have the potential to represent
complex networks and systems, and to impact nearly all industries (Cearley et al., 2016).
13 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
INNOVATION TRENDS
View real-time data on the working condition of equipment
Predict and monitor the conditions under which equipment might fail or break down
Virtually simulate, validate and optimise an entire system or process
EXPERT OPINION
WEARFUEL
WEARFUEL
WEAROUTPUT
WEAROUTPUT
WEAROUTPUT
WEAROUTPUT
STOREPOWER
TOTAL OUTPUT
TOTAL INPUT
WEARFUEL
USA
GERMANY
GENERAL ELECTRIC
SIEMENS
Experts were surveyed and asked to rate the impact of digital twin technology on a 5-point scale. On average, responses show that the capabilities offered by digital twin technology were perceived to have high impact for Australian rural industries. Particularly highly rated were the capabilities to view real-time data on the working condition of equipment, and to predict and monitor the conditions under which equipment might fail.
DIGITAL TWIN
Digital twin technology is driven by General Electric and Siemens. Digital twin innovation is likely to expand with the growth of the Internet of Things.
Digital twin technology has the potential to be implemented in agricultural processes to monitor equipment status, such as part wear and power input/output, and to test and facilitate optimal system performance.
Digital twin technology is at the confluence of advanced sensor technology, big data, and the Internet of Things. With assets in the real world represented by digital twins, it is possible for a user to monitor and control assets remotely. Over time, and with extensive data, digital twins have the potential to be transformative in many industries. They can be used to predict asset failure, or to simulate changes to a process before implementing them in the real world.
0Very low impact
1
2
3
4
Very high impact5
95%98%27%
25%98%
1.1MWh
1.1MWh56%45%
550KWh7%
370KWh21% 120KWh
56%
500KWh70%
1.5MWh
3.7
4.44.2
14 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
LOW POWER WIDE AREA NETWORKS
Low power wireless area networks (LPWAN) support low bandwidth communication
between sensors and smart devices (Velosa et al., 2016). Various LPWAN technologies
are available and in development, including LoRa, Sigfox, Weightless, Ingenu RPMA,
EC-GSM and NB-IoT. Due to the low bandwidth and subsequent low data transfer of
LPWANs, connected devices can be deployed across large areas and powered for
several years on a single battery (Adelantado et al., 2017).
APPLICATIONApplications enabled by LPWANs depend on device data transfer needs and the size
of the area in which they operate. Commercial LPWANs support the largest areas. The
subscription service, Sigfox, has stations around the world that support up to a 50km
range. NB-IoT and EC-GSM leverage existing licensed mobile network bands and will offer
coverage similar to cellular networks (Wixted et al., 2017). Use cases for such networks
include geotracking of vehicles and communication with geographically dispersed assets,
such as oil pumps and pipelines (Velosa et al., 2016). Other technologies, such as LoRa,
use unlicensed ISM bands, and can therefore be used to set up private networks that are
independent of a network operator (Velosa et al., 2016). This is an important capability
for applications such as agriculture which might be situated in areas not serviced by
reliable wireless connectivity.
LoRaWANs are created by gateways which relay messages between devices and a
central network server. Gateways have a range up to 15km in unobstructed environments
and several gateways can be deployed to cover a greater distance. LoRa technology
facilitates bi-directional communication and can support thousands of devices with
maximum data transfer of 27 kbps. It is best suited to applications that receive and send
a reduced number of regular or irregular messages. This makes it an attractive option for
applications such as agriculture, leak detection, and environmental control. LoRaWANs
are not suitable for industrial automation or critical infrastructure that require real-time
data transfer and monitoring (Adelantado et al., 2017).
FORECASTLoRa has a strong community in urban Europe, however, is less established in other
parts of the world. As an immature market, the LPWAN landscape is likely to change
with the roll-out of commercial networks and an increasing uptake of Internet of Things
for business and consumer applications. While commercial technologies are likely to
dominate the market, smaller players such as LoRa are likely to be persistent to support
specific applications.
15 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
0Very low impact
1
2
3
4
Very high impact5
Seamless interoperability among smart things without the need for complex local infrastructure
Inexpensive, low power networking of connected smart things without the need for broadband or mobile internet
EXPERT OPINION
LoRaWANGATEWAY
LoRaWANGATEWAY
Experts were surveyed and asked to rate the impact of LoRaWAN technology on a 5-point scale. On average, responses show that there is a strong interest in smart things, and the Internet of Things in Australian rural industries. Low power and inexpensive networks that are not reliant on complex local infrastructure were perceived to have high impact by participants.
27 88
SPAI
N
BELG
IUM
GERM
ANY
AUST
RALIA
USA
52INNOVATION TRENDS
22
ITALY
16
NETH
ERLA
NDS
201
UK
1041 13
SWITZ
ERLA
ND
Map showing the key centres for LoRaWAN networks and number of gateways established in each location.
Proposed uses of LoRaWAN technology in agriculture have been cattle tracking and soil quality monitoring. The application of LoRaWAN technology could extend to a variety of applications that use low power sensors and smart things.
LoRaWANLoRaWAN, and other LPWAN technology is a response to the growing Internet of Things. LoRaWAN technology is capable of providing low power, wide area networks for thousands of connected smart things. Of particular utility is that private LoRaWANs can be setup independent of network carriers. This enables it to be used for applications in remote contexts, such as agriculture, that do not have reliable wireless internet coverage.
4.44.2
16 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
HUMAN-MACHINE INTERFACES
This section provides a brief overview and forecast of human-machine interfaces.
Three emerging human-machine interfaces are then discussed in further detail: Natural
Language Interfaces, Conversational Systems, and Wearable User Interfaces.
A human-machine interface is a device or software that allows humans to operate
machines and technology. They are a broad class of technology, covering common
devices such as keyboard and mouse, and touch screen interfaces. Due to the ubiquitous
nature of machines and technology in our lives, most people will have some experience
with human-machine interfaces. As technology is becoming more sophisticated with
the growth of the Internet of Things and artificial intelligence, our interactions are
also changing. Human-machine interfaces are expanding to facilitate new methods of
interaction, such as voice and gesture.
17 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
FORECASTAs the range of applications, and devices that people interact with grow, novel methods
of human-machine interaction will increase in importance. It is believed that he next five
years will see significant innovation in human-machine interfaces (Cearley et al., 2016).
Billions of devices will implement zero-touch interfaces (Zimmermann et al., 2016).
Instead they will rely on other human senses such as sight and smell, as well as sensory
channels that extend beyond human senses, such as radar (Cearley et al., 2016).
Innovation of human-machine interfaces will be present across various industries, and
will be potentially transformative in industries relying on complex interactions between
humans and machines. With advanced human-machine interfaces, requirements for
computer literacy will diminish and smarter machines will make it easier for humans to
handle unfamiliar or complex situations (Brant & Austin, 2016). For instance, devices
and machines that have no visual interface, but that can carry out complex tasks based
on simple commands given by human workers.
As the number of connected devices grow, human-machine interfaces will also facilitate
ambient experiences. This is where machines and computerised devices can recognise
contextual information and then adapt experiences and actions as users passively move
through different locations or perform different tasks (Goertz & Reinhart, 2016).
18 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
NATURAL LANGUAGE INTERFACESNatural language interfaces facilitate interactions between humans and machines using
a range of input and output types, including sight, sound, touch, smell and radar. These
various interaction modalities are used to build a ‘conversation’ between the human and
machine to create an immersive, continuous and contextual experience (Cearley et al.,
2016). As technology advances in the areas of mobile computing, wearables and the
Internet of Things, zero-touch interactions between humans and machines will become
increasingly prevalent (Zimmermann et al., 2016).
APPLICATIONThe most differentiated applications facilitated by natural language interfaces will make
use of voice, ambient technology, biometrics, facial recognition, ‘sensored’ environments,
movements and gestures. Combining these technologies will allow for natural and
personalised interactions (Zimmermann et al., 2016). Interactions will be able to be
performed in multi-step sequences, and across multiple devices, systems and machines.
This will allow machines to understand user needs based on the tasks they are performing
as they move from place to place, and from data collected from previous tasks (Cearley
et al., 2016). The user will also be able to interact with the systems in meaningful ways
to instruct new actions (Brant & Austin, 2016).
These capabilities are demonstrated in the patent, mobile systems and methods of
supporting natural language human-machine interactions (Weider et al., 2016). The
functionality specified is for speech-based and non-speech-based interfaces for
telematics applications. Context, prior information, domain knowledge and user data
are used to create an environment for users to submit requests in a variety of domain
applications. The interface then responds to these requests in natural language. Extensive
profile information is stored for each user, thereby improving the capability to reliably
and accurately determine the context of the request and present expected results.
19 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
CONVERSATIONAL INTERFACESA conversational interface is a type of natural language interface which enables
interactions between a machine and a human to occur through spoken or written
language (Cearley et al., 2016). Well-established natural language interfaces include
digital assistants present on mobile devices and speaker products such as Google’s
Assistant, Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana.
APPLICATIONMainstream applications of conversational interfaces are still relatively primitive, allowing
people to ask basic questions or give basic commands (Brant & Austin, 2016) such
as make a phone call, set an alarm or schedule an appointment. Examples of this are
virtual assistants on smart phones and smart watches, and more recently on dedicated
hardware for the home, such as Amazon’s Echo speaker, and Google’s Home speaker.
These capabilities are also reflected in recent patents. For example, a patent for an
intelligent automated assistant is used to facilitate conversational natural language
interactions between a human and a computer on various platforms, including web and
mobile. These interactions are used to request various information or to request actions
be performed (Gruber et al., 2017). Similarly, technologies for conversational interfaces
for system control provides conversational control of a home automation system. Based
on spoken input, the computing device provides text response to the user and performs
the command given (Uppala et al., 2016). Interactions with conversational interfaces can
also be used to carry out very complex tasks. For example, collecting oral testimony from
crime witnesses which then results in the complex result of the creation of a suspect’s
image (Cearley et al., 2016).
In the last year, consumer applications of conversational interfaces have diversified
and spread to a variety of market segments (Crist, 2017). These include, but are not
limited to, televisions, fridges, robots, headphones, alarm clocks, automobiles (VW, Ford),
security systems, and lighting (Crist, 2017; Kastrenakes, 2017). A large part of this
diversification can be credited to Amazon’s decision to open its technology platform to
other vendors (Pierce, 2017). As the capabilities of conversational interfaces expand,
they will offer increasingly seamless and continuous interactions across different
products and devices in industry sectors as diverse as finance, retail, healthcare and
automotive (Bisht, 2016).
20 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
WEARABLE USER INTERFACESWearable user interfaces (WUIs) facilitate bidirectional communication between a
user and a computerised device. Communication can be passive or active. Passive
communication is where a wearable device picks up information from a user, technology
or context without direct input. This information can then be transmitted to other devices
or to the user directly. Active communication occurs where a user directly interacts with
the wearable device to issue commands or communicate with other technologies and
systems (Ghubril & Prentice, 2014).
APPLICATIONCurrent commercial applications of WUIs, such as smart watches and fitness trackers
are viewed to be marginally useful. The proliferation of Internet of Things devices will
lead to increasingly useful applications of WUIs, such as the ability to simultaneously
control a number of connected smart home devices (Ghubril & Prentice, 2014; Goertz
& Reinhart, 2016).
The most significant application of WUIs is in complex environments where users receive
and must be aware of simultaneous inputs from competing sources. In this use case,
WUIs are applied to reduce physical and cognitive workload. They can parse information
from peripheral devices and present it to the user, or they might be able to facilitate
simplified or automatic communication with peripheral devices. In military operations,
novel WUIs have been proposed to provide sensory enhancements for the identification
of critical features in an environment, and to aid navigation and communication (Laarni
et al., 2009). In the context of surgery, a patent for a wearable user interface for use with
ocular surgical console proposes a wearable device in communication with a surgical
console that simultaneously displays the surgical viewing area and peripheral data
relating to the surgery. This allows the surgeon to check settings and other information
without needing to pause the surgery to move their gaze (Yacono, 2014).
Other innovations are for directly controlling external computerised devices. A patent
for a method and apparatus for a gesture controlled interface for wearable devices
specifies a flexible device worn on the body comprising of sensors for detecting bio-
electrical signals and gestures. Triangulating inputs from each range of sensors allows
discriminations to be made between different gestures, subsequently allowing for the
control of a computerised device (Wagner, Langer, & Dahan, 2016).
21 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
0Very low impact
1
2
3
4
Very high impact5
100
20
30
4050
60
2017201620152014201320122011
1359
UK EP*
28 36
EURO
PEAN
PATE
NT O
FFICE
CHIN
A
JAPA
N
SOUT
H KO
REA
AUST
RALIA
USA
43
38
INNOVATION TRENDS3420
61 14
427
73
7
Hands free and non-verbal communication between human and machine facilitated by electronic wearable devices
Machines capable of understanding complex verbal commands and then carrying out relevant tasks
Interfaces that allow unskilled workers to operate complex equipment and carry out complex tasks
NATURAL LANGUAGE INTERFACES CONVERSATIONAL INTERFACES WEARABLE USER INTERFACES
EXPERT OPINION
Experts were surveyed and asked to rate the impact of human-machine interface technologies on a 5-point scale. On average, responses show that human-machine interfaces have potential impact for addressing skill shortages in rural workers, allowing unskilled workers to operate complex equipment. Interfaces that allow verbal communication between humans and machines were perceived to have higher impact than communication via electronic wearable devices.
Analysis of number of patents published by country identifies the USA as the main innovator of human-machine interfaces.
Analysis of number of patents published per year shows that innovation of natural language interfaces and conversational interfaces has occurred for several years, with peaks in 2014 and 2016. Innovation of wearable user interfaces has been less extensive, but more recent between 2014-2017.
HUMAN-MACHINE INTERFACES
Human-machine interfaces provide the means for humans and machines to communicate. With increasingly sophisticated technology and the growth of artificial intelligence, novel interfaces that accept input from various sensory channels will allow for unprecedented levels of communication between humans and machines.
3.63.5
3.3
22 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
SOLAR RETRANSMISSION
Aerospace technology plays an important role in precision agriculture. High resolution,
remote sensing imagery can be obtained at near real-time (e.g., planet.com) to provide
invaluable information about crop yield variability. This can be used to generate yield
maps in-season and to assist after-season management (Yang, Everitt, Du, Luo, &
Chanussot, 2013).
Beyond commercial services, there are even greater aspirations for how to leverage the
resources of space. Companies such as Planetary Resources (plantaryresources.com)
aim to unlock the solar system’s economy by mining asteroids for important elements.
The feasibility of such operations is highlighted by NASA’s Asteroid Redirect Mission
in 2020. This mission aims to retrieve a multi-ton boulder from a near-Earth asteroid,
and redirect it to a stable orbit closer to Earth where samples will be taken (NASA,
2015). Further, an area of sustained interest for aerospace technology is lunar-based
solar power. Various schemes have been proposed to capture solar energy in space and
beam it down to Earth for use (Bullis, 2009; Dorminy, 2017a, 2017b).
APPLICATIONWith potential impact and application for agriculture, a patent has recently been published
specifying a system for capturing and retransmitting solar energy for reinforcing
photosynthesis (Laine & Parrot, 2015). The technology comprises of a satellite with
two optical assemblies; the first for collecting sunlight, and the second for retransmitting
sunlight. The optical assembly for retransmission has a controllable orientation, meaning
that it could be positioned to retransmit sunlight to specific and modifiable locations on
Earth. Further, by transferring light through the optical assemblies only light in specified
frequency bands that are optimal for photosynthesis are retransmitted.
PREDICTIONThe aerospace industry is growing rapidly. In north America, the aerospace and defence
industry grew by 4.4% in 2016 to $365 billion. It is expected to reach $421 billion by
the end of 2021. Even faster growth is expected in the Asia-Pacific with the sector
growing 10.9% in 2016 to reach $272 billion. While much of this value is generated
through government contracts, the number of new private companies being created in
the commercial aerospace industry is increasing (Singh, 2014). Although the concepts
of lunar mining and solar retransmission are ideas ahead of their time, continued
commercial interest and innovation make them difficult to ignore.
23 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
GERMANY
0Very low impact
1
2
3
4
Very high impact5
Real-time, high-resolution satellite imagery with interactive capabilities (e.g., zoom, overlays and movement)
Control and optimise sunlight exposure and growing conditions for crops
Control the frequency of sunlight for optimal photosynthesis (450nm to 660nm)
SOLAR RETRANSMISSION
Solar capture/retransmission satellite
Solar capture
Solar retransmission
The use of satellites and the imagery they capture is an important asset for precision agriculture. Although ahead of its time, the notion of using satellites to capture solar energy and retransmit it to Earth represents an iteration of a persistent interest in utilising space-based resources for applications on Earth. This proposed innovation sits in the context of active work in the areas of lunar-based mining and solar energy.
3.8
4.1
4.4
EXPERT OPINION
Survey responses suggest Experts were surveyed and asked to rate the impact of satellite imagery and solar retransmission technology on a 5-point scale. On average, responses demonstrate the value of satellite imagery for agricultural applications. Although perceived to have lesser impact, the capabilities of proposed solar retransmission satellites were of interest to survey participants. The ability to control and optimise sunlight exposure and growing conditions for crops was perceived to have particular impact.
INNOVATION TRENDS
The below diagram provides a visual representation of the proposed solar retransmission system in the patent, a system for capturing and retransmitting solar energy for reinforcing photosynthesis (Laine & Parrot, 2015).
24 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
PERSONAL ANALYTICS
This section presents and overview and forecast for personal analytics technology. Two
emerging personal tracking technologies are then discussed: Labour Tracking, and
Smart Contact Lenses.
The term personal analytics describes technologies and practices that are used to
monitor personal activities and behaviours. Personal analytics is also referred to as
personal informatics and quantified self (Lupton, 2016). One of the most obvious types
of personal analytics occurs via fitness trackers and smart watch devices, such as Fitbit
and Apple Watch. These products are usually positioned as technologies that facilitate
self-improvement and a healthier lifestyle. They propose to do this by monitoring and
providing feedback on variables such as calories burned, distance and speed travelled,
sedentary vs active time, and sleep quality. Users are then expected interpret and act on
this data in meaningful ways (Delgado, 2015; Lupton, 2016).
Personal analytics technology has received great interest for tracking workers in
hazardous environments such as factories and job sites. In such contexts, the application
of personal analytics technology is aimed at reducing accidents, improving worker
accountability, identifying worker fraud, and optimising work efficiency (Scism, 2016).
Personal analytics technologies are also viewed as important for tracking individuals
with special needs such as the elderly and hospital patients (Janwadkar, Bhavar, & Kolte,
2016).
FORECASTBig data is becoming an increasingly sought after and valuable resource for businesses.
Customer insights data can provide highly profitable forms of knowledge to inform
changes to business strategy and operations (Lupton, 2016). With personal analytics
technology, it is possible to generate similar insights about the behaviours of human
resources to create safer work environments and optimised operations (Ghubril
& Prentice, 2014; Zimmermann et al., 2016). Such capabilities are believed to have
application in a range of industries including healthcare, agriculture, and security and
policing (Lupton, 2016). Personal analytics technologies will offer particular benefit for
industries where workers perform jobs alone, in hazardous conditions, and where there
is the potential to harm others. In 2020, it is expected that 50% of employees in isolated
and hazardous working conditions, such as heavy machinery operation, will be monitored
by wearable personal analytics technology (Zimmermann et al., 2016).
25 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
LABOUR TRACKINGLabour tracking is the application of personal analytics technologies and practices to the
tracking and monitoring of human resources in workplace contexts. Methods for labour
tracking are diverse. They range from camera surveillance systems that monitor task
efficiency, to sensor enabled wearable devices that can track biometric inputs including
fatigue and heart rate (O’Connor, 2016). With the development of the Industrial Internet
of Things, innovation of labour tracking is being driven by the convergence of big data
analytics and low-cost sensing (Intel, 2015). Workers connected by intelligent sensor
devices and big data analytics are not only safer, but measures can be implemented to
increase worker and operations efficiency (O’Connor, 2016; Scism, 2016).
APPLICATIONLabour tracking has significant applications in industries that are hazardous (Scism,
2016), require workers to perform tasks in remote or isolated locations (Zimmermann
et al., 2016), and that require high standards of accountability (Scism, 2016). In such
contexts, labour tracking technologies include simple wearable devices, such as fitness
trackers that provide feedback to an individual and an organisation. On a more complex
level, labour tracking solutions can simultaneously measure biometrics as well as
environmental data. For instance, a “combination of skin temperature, raised heart rate,
and no movement patterns for several minutes could mean a person is suffering from
heat stress” (O’Connor, 2016).
Current labour tracking systems include Vandrico’s (vandrico.com) smart helmet
wearable device used to monitor miners. This device clips on to standard helmets and
provides 3D real-time location tracking, and hands-free communication to workers via
visual cues. A recent patent (Daniel, 2017), provides a system for managing teams and
assets based on geofencing principles. Members of a team are equipped with wireless
tracking devices that establish a virtual perimeter based on the location of individuals.
It is possible to specify maximum distance between members and send/receive alerts
when this is exceeded. Such technology has potential in critical environments, or in jobs
where specific relationships between workers and equipment is required. For example,
to maintain efficiency, or to reduce asset shrinkage.
26 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
SMART CONTACT LENSESSmart contact lenses are technology embedded contact lenses that are capable of
sensing various biometric signals, as well as providing enhanced functionality such as
augmented reality. Worn in the same manner as regular contact lenses, smart contact
lenses can offer unobtrusive monitoring of individuals, as well as provide significant
visual enhancements. Smart contact lenses represents a broader trend of nanosensor
wearables, that incorporate sensors that range from millimetre to nanometre scale
(Garcia-Martinez, 2016). At this scale, sensors can fit in discrete and unobtrusive
personal analytics devices.
APPLICATIONNotable applications of smart contact lenses come from two giants of the tech industry;
Google and Samsung. Google, in partnership with pharmaceutical company Novartis,
announced that it is working on a smart contact lens that measures blood-glucose levels
for people with diabetes (King, 2014). Samsung, on the other hand, has recently filed a
patent for a smart contact lens featuring a screen for use in augmented reality applications
(Samsung Electronics Co Ltd, 2016). It is argued that this will provides a more natural
augmented reality experience than larger head mounted displays (Chowdhry, 2016).
Samsung is not alone in developing display integrated smart contact lenses. A patent
specifying a smart contact lens with embedded display and image focusing system was
recently published (Shtukater & Raayonnova, 2017). This technology claims to monitor
a person’s head movement and gaze direction, which could be particularly useful for
evaluating worker vigilance and fatigue.
The capabilities of smart contact lenses emphasise the potential application of nano-
scale personal tracking devices. While smart contact lenses won’t be appropriate for all
people and industries, the development of discrete nanosensor tracking devices in other
form factors will have significant impact. Examples might be discrete and disposable
patches worn on the skin to detect fluctuations in temperature or environmental
contaminants. For this purpose, some of the most advanced nanosensors to date
have been ‘manufactured’ using synthetic biology and the modification of single-celled
organisms (Garcia-Martinez, 2016). Known as biosensors, these nano-scale biological
sensors are customisable to detect various types of chemical and biometric targets,
such as skin conductivity, pH, and temperature (Edwards et al., 2013).
27 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
0Very low impact
1
2
3
4
Very high impact5
CANA
DA
200
40
60
80100
120
2017201620152014201320122011
2048
47
13
UK EPO*
26 99
EURO
PEAN
PATE
NT O
FFICE
CHIN
A
JAPA
N
IB*
INTER
NATIO
NAL
BURE
AU O
F IP
SOUT
H KO
REA
AUST
RALIA
USA
16 33
7
12
4INNOVATION TRENDS
85 96
24-hour monitoring of worker health, including fatigue, stress and alertness
Communication of real-time personal health data to employers and medical practitioners
Track the location of workers in 3D space to protect them from hazards and reduce worker fraud
Track and monitor workers over time to optimise work schedule and increase productivity
EXPERT OPINION
Experts were surveyed and asked to rate the impact of personal analytics technologies on a 5-point scale. On average, personal analytics technologies are perceived to have greatest impact for the purpose of monitoring workers. The ability to track worker location to increase productivity and safety, and to monitor stress and fatigue were highly rated. Comparatively, communication of this data to health professionals was perceived to have less impact.
Analysis of patents published by country identifies the USA as the main innovator of labour tracking and smart contact lense technology. However, innovation has occured in Europe and Asia.
Analysis of number of patents published per year shows that innovation of labour tracking technology has steadily increased over the last several years. Innovation of smart contact lenses began in 2014 and has increased in 2016 and 2017.
LABOUR TRACKING SMART CONTACT LENSESPERSONAL ANALYTICS
The use of personal analytics technologies has gained increasing interest in work contexts to improve worker safety and increase operations efficiency. Advancements in wearable and nonosensor technology is driving innovation in unobtrusive personal analytics technology.
4
3.7
3.33.6
28 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
CONCLUSION
This report represents the first iteration in a revised horizon scanning scope. This revised
scope was initiated by the learnings gained from Horizon Scan 1, and targets the early
detection of emerging and transformative technologies that have potential impact for
Australian rural industries. Identification of technologies having impact internationally
and beyond the rural industries was highlighted as having particular value. To meet the
aims of the revised scope, our approach incorporates data mining, synthesis, industry
experts and visualisation across three interactive streams: discovery, evaluation, and
consolidation. The progression and interactivity of these streams has been designed
to detect emerging technologies across a broad range of domains, to understand the
value and potential of these technologies, and then extrapolate findings to present their
significance for Australian rural industries. The success of this approach is evident in the
outcomes that have been produced and discussed in this report.
In this first iteration, category and discrete technology types have been identified in
domains as diverse as information and communication technology, data analytics,
genomics, artificial intelligence, and renewable energy. Through our evaluation stream, a
sub-set of these technologies has been refined, leading to the 8 analysed and discussed
in this report. These 8 represent various types of innovation and potential impact for
Australian rural industries. For each of the technologies discussed in this report, we
have attempted to clearly communicate the essential elements of the technology,
current application areas, innovation trends, and an initial forecast for the continued
development and significance of the technologies. This process has principally been
29 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
achieved through data mining of patent data bases, and the synthesis of this and other
data sources. Further, the capabilities of each technology have been recontextualised
for Australian rural industries through engagement with industry experts, and in some
cases through early stage scenarios.
The extent of technologies identified, and their refinement and synthesis has provided
a demonstration of the utility of the discovery, evaluation and consolidation project
streams. As this report represents one iteration of each of these streams, our aim for
future reports is to build on the capabilities of each stream. Of particular importance
in this respect is the further extrapolation of our findings to clearly communicate the
transferability and subsequent impact of technologies for the Australian rural industries.
In the following reporting periods, we aim to implement multiple cycles of discovery,
evaluation and consolidation. In each successive iteration of this process, we will:
• Broaden our scanning scope to ensure coverage of technologies from a broad range
of domains.
• Expand upon the already identified technologies, and evaluate their impact for
Australian rural industries.
• Broaden data mining sources to strengthen the analysis of technology trends.
• Continue to monitor and develop knowledge of the technologies that have already
been identified to have potential impact for Australian rural industries.
• Continue engagement with QUT and RIRDC experts to gain insights into the specific
value and use cases of the identified technologies, and clearly communicate these
evaluations in infographic and scenario visualisations.
30 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
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Dorminy, B. (2017a). Physicist Wants To Beam Solar Energy Back From Moon’s Surface. Retrieved April 3, 2017, from https://www.forbes.com/sites/brucedorminey/2017/03/28/physicist-wants-to-beam-solar-energy-back-from-moons-surface/#66b4ce115743
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APPENDIX: APPROACH AND PROJECT STREAMS
This project employs a concurrent phase, iterative methodology to:
1. scan and extract data from a variety of sources;
2. synthesise this information to identify potential trends and innovations for further
investigation;
3. and, communicate these issues through visualisations.
Implementation of each respective project phase, led by data mining, synthesis, and
visualisation, occurs across three interactive streams: Discovery; Evaluation; and,
Consolidation. These streams are continuous over the course of the project requiring
different inputs from each project phase. These inputs from each phase, and their
interactivity is represented in Figure 3.
FIGURE 3. PROJECT INPUTS AND STREAM INTERACTION
Sourc
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Sourc
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Surve
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Synth
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DISCOVERYEVALUATIONCONSOLIDATION
Visua
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Synth
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35 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
DISCOVERYDiscovery focused on scanning selected sources to extract relevant data and detect
signals of emerging and transformative technologies. Data sources were strategically
selected to ensure input from a diversity of domains within an international context.
In this implementation of the discovery stream, sources included select twitter users,
patent databases, industry and market reports, and technology news media. Scanning
of these sources has employed data mining and synthesis concurrently:
• Data mining focused on scanning the twitter feeds of thought leaders and technology
experts in a range of technology and industry domains. These were selected based
on the recommendations of QUT experts working in relevant technology domains.
• Synthesis focused on scanning a broad range of technology news publishers,
industry and market reports, and patent databases.
The confluence of data mining and synthesis outputs in the discovery stream resulted
in a list of technologies for further investigation in the subsequent evaluation and
elaboration streams.
EVALUATIONEvaluation focused on filtering the signals identified in the discovery stream. Its
implementation leveraged expertise from QUT, and RIRDC through the provided list
of innovative farmers, to assess the potential impact of a broad range of emerging
technologies. This process used Delphi style surveys which are deployed in successive
rounds.
The first round of surveys focused on evaluating a large volume of technologies identified
in the discovery stream. This was achieved through presenting a series of simple
statements that describe the functionality of a technology, and asking for a rating based
on its perceived impact. From these ratings, a short-list of technologies was compiled
for further investigation and ongoing monitoring. Successive survey rounds will present
a lower volume of questions but will require increasingly qualitative responses. It is
through this process that we will narrow the scope of technologies that will be included
on the watchlist and that will be monitored throughout future reporting periods. As the
evaluation stream progresses, we will develop an objective list of criteria to establish the
possible scale of impact of each transformative technology. These criteria will then be
re-introduced in successive discovery stages of the project to assist with filtering the
large volume of data.
36 HORIZON SCAN 2 TRANSFORMATIVE TECHNOLOGY
CONSOLIDATIONConsolidation focused on monitoring technologies on the initial watchlist, and providing
a detailed analysis of their potential impact for Australian Rural Industries. This involved
a detailed analysis of the technologies’ potential impact for Australian rural industries
and developing a rationale for why they should be monitored. This elaboration stream
utilises both data mining and synthesis to receive input from a broad range of data, and
gain the requisite detail.
Data mining focuses on eliciting deeper analysis of the technologies identified and short-
listed during the discovery and evaluation streams. Directed by specific technologies
and related keywords, data mining targets social media feeds and patent databases. The
outcome of this stage provides assessment of candidate technologies based on metrics
such as trends over time, associated keywords and industries to identify the contexts in
which the technology is active and where it is receiving innovation, and location.
The data gained from this stage of data mining, and from the evaluation stream, feeds into
and directs synthesis. With this direction, synthesis can focus on specific applications and
implementations of the technology to better understand and communicate its potential
impact for Australian rural industries. Visualisation is then used to bring together the
inputs of synthesis and data mining, as well as inputs from the evaluation stream. This
includes the development of infographics to communicate the watchlist issues and
themes.